microsoft/TimeCraft

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1085 stars · 65 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TimeCraft is a diffusion model-based framework for generating high-quality synthetic time series data across domains. It uses learned semantic prototypes and a Prototype Assignment Module to adapt to new domains with few-shot examples, enabling controllable, privacy-preserving generation.

Use cases

• generate synthetic time series data
• augment scarce time series datasets
• create privacy-preserving synthetic sensor data
• simulate time series for forecasting model training
• generate domain-specific time series with few-shot examples
• produce controllable synthetic trends and seasonality

When to choose

• you need synthetic time series for domains with limited real data
• privacy constraints prevent sharing real time series
• you want controllable generation of trends or seasonality
• you need cross-domain generalization without retraining from scratch

When to avoid

• you need simple classical time series forecasting rather than generation
• you require production-hardened tooling rather than research code
• your data is not temporal in nature
• you lack GPU resources for diffusion model training or inference

Facets

library · maturity active

machine-learning data-generation deep-learning time-series machine-learning data-science python cross-platform diffusion-models synthetic-data time-series-generation generative-ai research-code

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markdown · JSON · MCP: product_card(name="microsoft/TimeCraft")